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What is Overfitting in Machine Learning? Explained Simply
Overfitting is when a model memorizes training data but fails on unseen data. Learn how to spot it and why it matters for machine learning accuracy.
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Main Challenges in Machine Learning: 1. Insufficient or Poor-Quality Data Lack of labeled data for supervised learning. Noisy, incomplete, or biased data can lead to poor models. 2. Overfitting and Underfitting Overfitting: Model performs well on training data but poorly on new data. Underfitting: Model is too simple to capture the underlying pattern. 3. High Computational Cost Training complex models (e.g., deep learning) requires powerful hardware and GPUs. 4. Scalability Models trained on sma
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